Retinal imaging is one of the important means of medical aided diagnosis and treatment, which can directly or indirectly determine a variety of ocular diseases by analyzing eyeball blood vessel images. In ocular images, there exist a variety of retinal vessels with varying degrees of thickness, more clear and accurate retinal vessel images can be obtained by enhancing these images, thereby facilitating aided clinical diagnosis.
There are many methods for retinal vessel image enhancement, in general, the most frequently used methods are as follows:
Neighborhood smoothing method, it calculates an average between grayscale of a certain pixel and that of its neighbors in an image, and uses the average value as the grayscale value of the pixel. The advantage of this method is its simplicity, and its disadvantage is that it can make the retinal vessel image blurred, significantly decreasing the clarity of blood vessels.
Edge-preserving smoothing method, namely design different templates, and calculate variances of the grayscale of neighborhood pixels to a certain pixel point in an image, then select a template with minimum variance and take the average grayscale value of pixels contained in the template as the grayscale value of the pixel. The advantage of this method is that it can preferably preserve edges, and its disadvantage is that the target is a linear structure in the retinal vessel image, it is difficult to distinguish noise from target by analysis of variance.
Multiple images averaging method, it chooses multiple images of eyeball blood vessels taken from the same person, and performs the average processing. The advantage of this method is that it can suppress noise to a certain extent, and its disadvantage is that it requires multiple images of eyeball blood vessels, not applicable to a single retinal vessel image.
Frangi-based filtering image enhancement, it enhances a linear structure using eigenvector directions and eigenvalues of Hessian matrix in the linear structure, however, such methods can lead to loss of gracile and thin vessels.
Image denoising methods based on sparse representation, they can get a redundant dictionary by training, then reconstruct the original image according to sparse coefficients, thereby obtain a noise-suppressed image due to no noise in the selected dictionary atoms. This method possesses better noise suppression effects, however, there still exists a problem of loss of gracile and thin vessels when it is applied to the issue of retinal vessel image enhancement.
Thus it can be seen that the existing methods for retinal vessel image enhancement are not able to better reserve thin vessels at the same time of enhancing retinal vessels.